Course detail
Knowledge Discovery in Databases
FIT-ZZDAcad. year: 2018/2019
- The deepening of basics in KDD - basics of methods of data preprocessing (statistics quantities used in data summarization, approaches to data cleaning, transformation and reduction), basics of data warehousing, basic methods and algorithms of mining frequent items and patterns and association rules (Apriori algorithm, FP-tree, multi-level association rules, mining multidimensional association rules from relational databases), basic methods and algorithms of classification (decision tree, Bayesian classification, using neural networks, SVM) and prediction (linear and nonlinear regression), basic methods and algorithms of cluster analysis (distance of data, partitioning methods, hierarchical methods, CF-tree, density-based methods, grid- and model-based methods).
- Advanced data mining techniques - advanced techniques of data mining in 'classic' data sources, mining in data streams, time series and sequences, mining in biological data; mining in graphs, multirelational data mining, mining in object, spatial and multimedia data, mining in text, mining on the Web.
Language of instruction
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
Bishop, CH. M.: Pattern Recognition and Machine Learning. Springer, 2006, 738 p. ISBN 978-0-387-31073-2.
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p. ISBN 1-55860-901-3.
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Third Edition. Elsevier Inc., 2012, 703 p. ISBN 978-0-12-381479-1.
Papers in journals and conference proceedings (including those in ACM Digital library, IEEE Digital library and other electronic sources).
Classification of course in study plans
- Programme CSE-PHD-4 Doctoral
branch DVI4 , 0 year of study, winter semester, elective
- Programme CSE-PHD-4 Doctoral
branch DVI4 , 0 year of study, winter semester, elective
- Programme CSE-PHD-4 Doctoral
branch DVI4 , 0 year of study, winter semester, elective
- Programme CSE-PHD-4 Doctoral
branch DVI4 , 0 year of study, winter semester, elective
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Data preprocessing.
- Data warehousing.
- Asociation analysis.
- Classification and prediction.
- Cluster analysis.
- Advanced data mining in 'classic' data sources.
- Mining in data streams.
- Data mining in time series and sequences.
- Mining in biological data.
- Data mining in graph structures.
- Mining in object, spatial and multimedia data.
- Text mining and Web mining.
- Mining moving object data.
Project
Teacher / Lecturer
Syllabus
- Reading up and treatment of a selected topic concerning knowledge discovery in a field related to the student's PhD thesis.
Guided consultation in combined form of studies
Teacher / Lecturer